DeepFace: Closing the Gap to Human-Level Performance in ...

DeepFace: Closing the Gap to Human-Level Performance in Face VerificationYaniv TaigmanMing YangMarc Aurelio RanzatoFacebook AI ResearchMenlo Park, CA, USA{yaniv, mingyang, WolfTel Aviv UniversityTel Aviv, modern face recognition , the conventional pipelineconsists of four stages: detect align represent clas-sify. We revisit both the alignment step and the representa-tion step by employing explicit 3D face modeling in order toapply a piecewise affine transformation, and derive a facerepresentation from a nine-layer deep neural network. Thisdeep network involves more than 120 million parametersusing several locally connected layers without weight shar-ing, rather than the standard convolutional layers. Thuswe trained it on the largest facial dataset to-date, an iden-tity labeled dataset of four million facial images belong-ing to more than 4,000 identities.}

ing human-level performance. 1. Introduction Face recognition in unconstrained images is at the fore-front of the algorithmic perception revolution. The social and cultural implications of face recognition technologies are far reaching, yet the current performance gap in this do-main between machines and the human visual system serves

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  Performance, Recognition

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